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This document describes results from the OHI 2018 global assessment.

Description of reference datasets and figures

Primary dataset

Data are the OHI scores for the eez regions of 220 countries and territories from 2012 to 2018.

Region 0 is the global average scores, which are calculated using an area-weighted average of the region scores (status, likely future state, and score).

Figures that provide an overview of scores

This carpet plot figure (download as high resolution png) provides a full overview of the scores from the 2018 assessment. Each row represents a region, the main groupings represent goals, and within each goal there are 6 years of data. Black regions indicate no data.

Don’t try too hard to interpret the results for specific countries/goals/years!!

This plot is good for providing a quick overview of things like:

  • What is the range of scores?
  • Which goals tend to have high scores across most regions (species, habitat)
  • Which goals have a lot of variation across regions (tourism & recreation, lasting special places)
  • Which goals are volatile across years (natural products, tourism & recreation)

Another resource that can be useful for examining scores is this interactive plot. This can be used to (some example screen shots):

explore the distribution of scores

compare different goal scores

observe change over time

More figure resources

  • The location of maps describing goal and index scores can be downloaded from here
  • Flower plots for each region can be downloaded from here

Overview of global scores

This section describes global patterns in index and goal scores. The overall global score was 70.08.

Map of index scores

Map png files can be downloaded here.

(Maps of goal scores are described below)

Average global performance of the 10 goals:

Distribution of scores

The median index score was 66.89. The highest score was 91 for Howland Island and Baker Island, and the lowest score was 41 for Ivory Coast.

The following histogram describes the distribution of overall index scores:

The regions with index scores of 80 or greater are:

region_name Index
Howland Island and Baker Island 90.94
Heard and McDonald Islands 90.69
Kerguelen Islands 90.61
Pitcairn 85.01
Macquarie Island 84.65
South Georgia and the South Sandwich Islands 84.24
Norfolk Island 83.89
Christmas Island 83.80
New Caledonia 82.70
Jarvis Island 81.79
Madeira 81.76
Aruba 81.60
Phoenix Islands (Kiribati) 81.27
Germany 80.99
Jan Mayen 80.96
Wake Island 80.60
New Zealand 80.49
Cocos Islands 80.36
Palmyra Atoll 80.09

The regions with index scores of 50 or less are:

region_name Index
Guinea 49.96
Eritrea 49.60
Bosnia and Herzegovina 49.45
Guinea Bissau 49.44
Syria 48.46
Somalia 48.45
Sierra Leone 48.13
Democratic Republic of the Congo 46.94
Lebanon 46.35
Nicaragua 44.85
Libya 42.89
Ivory Coast 41.29

This interactive table describes the index and goal scores for the regions in 2018 (and here’s a link to a color coded table, and a csv file can also be downloaded).




Change over time

A color-coded table of 6 year trends is available here (and a csv file).

These values are calculated using a linear model of scores for each region/goal over the past 7 years. Positive values indicate potentially increasing scores during the past 7 years and negative values indicate potentially decreasing scores.

NOTE: Currently, these data include the livelihoods and economies goal but this trends should probably be calculated without this goal.

Exploring change over time of goals

The following table provides the global scores (eez area weighted average of region scores) for the Index and goals/subgoals.

(NOTE: Livelihood and economy goals are not included here)

Plot View

Table View

goal 2012 2013 2014 2015 2016 2017 2018
Index 71.11 71.46 71.44 70.83 70.39 70.10 70.08
Artisanal opportunities 76.75 77.09 77.36 77.45 77.55 77.66 77.87
Species condition (subgoal) 89.77 89.75 89.77 89.79 89.87 89.89 89.94
Biodiversity 90.21 90.22 90.47 90.55 90.64 90.53 90.63
Habitat (subgoal) 90.63 90.68 91.17 91.31 91.39 91.17 91.32
Coastal protection 88.16 87.71 87.68 87.57 87.16 86.40 85.97
Carbon storage 79.16 79.09 79.12 79.12 79.16 79.18 79.19
Clean water 70.75 70.41 70.53 70.80 70.86 70.98 71.06
Fisheries (subgoal) 56.88 58.45 58.29 55.31 52.72 51.05 50.60
Food provision 55.97 57.33 57.16 54.47 51.92 50.61 50.36
Mariculture (subgoal) 18.67 19.00 19.60 20.58 21.28 21.44 21.91
Iconic species (subgoal) 66.67 67.79 67.88 67.89 66.89 66.77 66.84
Sense of place 62.18 62.70 63.07 63.19 62.94 63.77 63.82
Lasting special places (subgoal) 57.68 57.61 58.26 58.50 58.99 60.76 60.80
Natural products 52.18 52.12 51.85 50.35 48.97 46.14 45.43
Tourism & recreation 53.64 52.18 51.27 48.90 48.70 49.26 49.58

The following is a very preliminary analysis of how the global scores changed over time. Global index scores decreased by an average -0.25 points per year (although, not significant with this rough analysis), with 142 regions having decreasing trends and 79 having increasing trends.

Declining scores

  • Fisheries declined by nearly 1.4 points per year on average, which is a greater decline rate than it was last year. Previous years before that had increasing trends (p = 0.004, 148 regions with decreasing trend and 71 with increasing trend)
  • Natural products declined by about 1.25 points per year on average, which is a greater rate of decline than last year (p = 0.0010, 90 regions with decreasing trend and 48 with increasing). However, there is a very large variation in trends for this goal (i.e., large standard deviation 7.44), indicating that some regions have had relatively large increases.
  • Tourism and recreation declined by about 0.74 points per year on average, which is a slower rate of decline than it was last year (p = 0.018, 111 regions with decreasing trend and 68 with increasing). However, this goal had a fairly large amount of variation in trend results.
  • Coastal protection declined by 0.35 points per year on average, which is greater than it was last year (p = 0.0008, 83 regions with decreasing trend and 27 with increasing). However, there is a fairly large amount of variation suggesting a few regions may be driving this trend, due to seaice loss.
  • Iconic species (sub-goal) declined by 0.09 points per year on average (p = 0.46, 136 countries with decreasing trends, 76 increasing). Small variation

Improving scores

  • Carbon storage increased by 0.01 per year on average (p = 0.11, 43 regions with increasing trend and 54 decreasing)
  • Species condition (sub-goal) increased by 0.03 per year on average (p = 0.002, 110 countries with increasing trend and 74 countries decreasing)
  • Biodiversity increased by 0.07 per year on average (p = 0.01, 111 countries with increasing trend and 82 countries decreasing)
  • Livelihoods increased by 0.07 per year on average (p = 0.11, 75 countries with increasing trend and 69 countries decreasing)
  • Clean waters increased by 0.09 per year on average, in contrast to decline by 0.11 points last year (p = 0.03, 101 regions with increasing trend and 107 with decreasing)
  • Habitat (sub-goal) increased by 0.12 per year on average (p = 0.03, 79 regions with increasing trend and 77 with decreasing)
  • Artisanal opportunities scores increased by 0.17 points per year on average, about the same as last year (p = 0.0003, 181 regions with increasing trend and 31 with decreasing)
  • Sense of place increased by 0.25 points per year on average (p = 0.003, 91 regions with increasing trend and 121 with decreasing)
  • Livelihoods and economies increased by 0.38 points per year on average (p = 0.14, 133 regions with increasing trend and 42 with decreasing)
  • Mariculture (sub-goal) increased by 0.58 points per year on average (p = 0.00005, 52 regions with increasing trend and 44 with decreasing)
  • Lasting special places scores increased by an average of 0.59 points per year, at a significantly greater rate of change than last year (p = 0.001, 66 regions with increasing trend and 45 with decreasing)
  • Economies increased by 0.70 points per year on average (p = 0.14, 116 regions with increasing trend and 21 with decreasing)
long_goal average_change_per_year p_value num_rgns_pos_trend num_rgns_neg_trend mean_trend sd_trend
Fisheries (subgoal) -1.40 0.0042789 71 148 -1.06 2.01
Food provision -1.27 0.0043006 76 143 -1.00 2.00
Natural products -1.25 0.0008809 48 90 -2.15 7.44
Tourism & recreation -0.74 0.0180107 68 111 -0.47 2.25
Coastal protection -0.35 0.0008195 27 83 -0.41 2.23
Index -0.25 0.0069813 79 141 -0.28 0.76
Iconic species (subgoal) -0.09 0.4556476 76 136 -0.09 0.34
Carbon storage 0.01 0.1094596 43 54 -0.01 0.08
Species condition (subgoal) 0.03 0.0020106 110 74 0.02 0.08
Biodiversity 0.07 0.0092038 111 82 0.01 0.44
Livelihoods 0.07 0.1135875 75 69 -0.05 1.09
Clean water 0.09 0.0322971 101 107 -0.03 0.86
Habitat (subgoal) 0.12 0.0275651 79 77 0.00 0.87
Artisanal opportunities 0.17 0.0002681 181 31 0.19 0.38
Sense of place 0.25 0.0029183 91 121 0.11 0.95
Livelihoods & economies 0.38 0.1362157 133 42 0.25 0.94
Mariculture (subgoal) 0.58 0.0000485 52 44 0.10 1.31
Lasting special places (subgoal) 0.59 0.0014393 66 45 0.31 1.89
Economies 0.70 0.1392107 116 21 0.55 1.26

Comparing assessment years

The following is a comparison of the global status scores generated for the 2017 scenario by this year’s assessment vs. last year’s assessment.

If the models and source data remains the same, these scores should be exactly the same. Differences indicate changes in methods or source data.

These changes do not reflect changes in actual system health!

Global averages

We made a few changes to some goals, such as the addition of edible seaweed species to Mariculture and removal of edible seaweed species from Natural Products. Furthermore we changed the data source used for Fisheries from SAUP to Watson.

Changes in scores for the 2017 scenario were..

The largest changes were for CW (-2.57) and MAR (-6.33). The change in mariculture reflects additions to source data, mentioned above.

Other regions changed by less than 2 points.

goal assess2018 assess2017 change
Artisanal opportunities 73.80 73.93 -0.13
Species condition (subgoal) 90.37 90.37 0.00
Biodiversity 89.59 89.35 0.24
Habitat (subgoal) 88.82 88.33 0.49
Coastal protection 84.77 84.77 0.00
Carbon storage 78.42 78.42 0.00
Clean water 70.88 73.45 -2.57
Economies 87.65 87.65 0.00
Livelihoods & economies 82.46 82.46 0.00
Livelihoods 77.28 77.28 0.00
Fisheries (subgoal) 49.16 48.34 0.82
Food provision 48.84 49.13 -0.29
Mariculture (subgoal) 19.60 25.93 -6.33
Iconic species (subgoal) 62.89 62.13 0.76
Sense of place 60.80 59.45 1.35
Lasting special places (subgoal) 58.70 56.77 1.93
Natural products 42.68 42.16 0.52
Tourism & recreation 48.02 46.51 1.51

Light blue is status for the 2017 assessment, and the dark blue is the status for the 2018 assessment.

Regional data

This color-coded table compares how the 2017 scenario index scores for each region/goal changed from the 2017 to 2018 assessment.

Because these are index scores, changes reflect updates to pressure and resilience scores as well as status.

The following interactive plot provides an overview of how the 2017 scenario scores changed between the 2017 and 2018 assessment for all goals and dimensions.

A closer look at goals

This section takes a closer look at each goal/subgoal.

Artisanal opportunities

Scores

Top 10 performers

region_name score
Norway 100.00
United Arab Emirates 100.00
Qatar 100.00
Kuwait 100.00
United States 100.00
Ireland 100.00
Brunei 100.00
Singapore 100.00
Cayman Islands 99.62
Bermuda 99.22

Bottom 10 performers

region_name score
211 Sao Tome and Principe 46.44
212 Solomon Islands 45.31
213 Cameroon 45.26
214 Benin 44.51
215 Guinea 44.48
216 Madagascar 43.89
217 Comoro Islands 43.84
218 Mozambique 43.49
219 Togo 43.29
220 Liberia 42.89

Species condition

Scores

Top 10 performers

region_name score
Saint Helena 97.93
Ascension 97.81
Northern Saint-Martin 97.70
Saba 97.69
French Guiana 97.59
Montserrat 97.56
Guadeloupe and Martinique 97.27
Aruba 97.14
Namibia 97.00
Tristan da Cunha 96.93

Bottom 10 performers

region_name score
211 Myanmar 80.40
212 East Timor 80.15
213 Libya 80.11
214 Saudi Arabia 80.10
215 Oecussi Ambeno 79.65
216 Singapore 78.73
217 Eritrea 78.06
218 Iraq 77.40
219 Bahrain 76.86
220 Sudan 75.02

Habitat

Scores

Top 10 performers

region_name score
Howland Island and Baker Island 100
Heard and McDonald Islands 100
Kerguelen Islands 100
Pitcairn 100
Macquarie Island 100
South Georgia and the South Sandwich Islands 100
Norfolk Island 100
Clipperton Island 100
Amsterdam Island and Saint Paul Island 100
Prince Edward Islands 100

Bottom 10 performers

region_name score
209 Ivory Coast 62.88
210 Senegal 62.64
211 Dominica 62.21
212 Belize 61.45
213 Iceland 61.28
214 Poland 59.66
215 Sierra Leone 56.25
216 Nigeria 55.13
217 Bosnia and Herzegovina 49.65
218 Jan Mayen 37.95

Coastal protection

Scores

Top 10 performers

region_name score
Howland Island and Baker Island 100
Pitcairn 100
Phoenix Islands (Kiribati) 100
Wallis and Futuna 100
Netherlands 100
Denmark 100
British Indian Ocean Territory 100
Tuvalu 100
Line Islands (Kiribati) 100
Saba 100

Bottom 10 performers

region_name score
161 Sierra Leone 31.55
162 Guinea 31.22
163 Senegal 30.38
164 Nicaragua 30.12
165 Guinea Bissau 30.10
166 Democratic Republic of the Congo 29.84
167 Sweden 29.33
168 Dominica 26.53
169 Belize 23.57
170 Lithuania 18.57

Carbon storage

Scores

Top 10 performers

region_name score
Germany 100
Northern Saint-Martin 100
Seychelles 100
Netherlands 100
Denmark 100
Russia 100
Morocco 100
Saba 100
French Guiana 100
Puerto Rico and Virgin Islands of the United States 100

Bottom 10 performers

region_name score
139 Liberia 34.51
140 Senegal 33.57
141 Guinea 32.81
142 Sierra Leone 32.61
143 Ivory Coast 32.39
144 Guinea Bissau 30.37
145 Democratic Republic of the Congo 29.79
146 Barbados 26.96
147 Dominica 26.70
148 Nicaragua 9.76

Clean waters

Scores

Top 10 performers

region_name score
Heard and McDonald Islands 99.71
Falkland Islands 98.96
Bouvet Island 98.91
South Georgia and the South Sandwich Islands 98.89
Kerguelen Islands 98.78
Jarvis Island 98.38
Macquarie Island 98.16
Phoenix Islands (Kiribati) 97.97
Howland Island and Baker Island 97.91
Crozet Islands 96.69

Bottom 10 performers

region_name score
211 China 29.76
212 Slovenia 28.16
213 Nigeria 28.10
214 Monaco 24.45
215 Bangladesh 23.67
216 India 22.70
217 Gibraltar 16.14
218 Togo 0.00
219 Gilbert Islands (Kiribati) 0.00
220 Benin 0.00

Fisheries

Scores

Top 10 performers

region_name score
Clipperton Island 94.35
Heard and McDonald Islands 93.35
Kerguelen Islands 91.45
Pitcairn 89.77
Finland 88.10
Wake Island 87.23
Howland Island and Baker Island 87.06
Estonia 83.65
Latvia 80.77
Northern Mariana Islands and Guam 79.76

Bottom 10 performers

region_name score
211 North Korea 15.66
212 Somalia 15.21
213 Suriname 15.17
214 Guadeloupe and Martinique 14.16
215 Vietnam 14.04
216 Myanmar 13.28
217 Guyana 12.69
218 Sint Eustatius 12.55
219 Samoa 12.01
220 Haiti 10.36

Mariculture

Scores

Top 10 performers

region_name score
Norway 100.00
South Korea 100.00
Chile 100.00
Ecuador 100.00
Faeroe Islands 100.00
North Korea 100.00
China 100.00
New Zealand 96.81
Iceland 91.38
Russia 58.89

Bottom 10 performers

region_name score
191 Guinea 0
192 Eritrea 0
193 Guinea Bissau 0
194 Syria 0
195 Somalia 0
196 Sierra Leone 0
197 Democratic Republic of the Congo 0
198 Lebanon 0
199 Libya 0
200 Ivory Coast 0

Iconic species

Scores

Top 10 performers

region_name score
Finland 95.83
Latvia 95.45
Poland 95.45
Lithuania 95.45
Estonia 95.00
Denmark 90.71
Germany 90.28
Sweden 89.78
Norfolk Island 88.78
Australia 85.12

Bottom 10 performers

region_name score
211 Crozet Islands 51.56
212 Amsterdam Island and Saint Paul Island 51.46
213 Prince Edward Islands 50.81
214 Jordan 50.34
215 Cambodia 48.77
216 Bahrain 48.17
217 Iraq 47.03
218 Bouvet Island 44.84
219 Heard and McDonald Islands 44.82
220 Monaco 36.57

Lasting special places

Scores

Top 10 performers

region_name score
Howland Island and Baker Island 100
Heard and McDonald Islands 100
Kerguelen Islands 100
Macquarie Island 100
South Georgia and the South Sandwich Islands 100
Norfolk Island 100
Jarvis Island 100
Madeira 100
Phoenix Islands (Kiribati) 100
Germany 100

Bottom 10 performers

region_name score
211 Jordan 0
212 Bahrain 0
213 Togo 0
214 Gilbert Islands (Kiribati) 0
215 Sudan 0
216 Iraq 0
217 Benin 0
218 Eritrea 0
219 Syria 0
220 Libya 0

Natural products

Scores

Top 10 performers

region_name score
New Caledonia 99.98
French Polynesia 98.03
Italy 95.60
Bangladesh 95.01
Indonesia 92.67
Philippines 92.42
South Korea 90.85
Latvia 88.96
North Korea 83.05
Cuba 82.85

Bottom 10 performers

region_name score
129 Mayotte 0
130 Brunei 0
131 Saint Lucia 0
132 Equatorial Guinea 0
133 Sao Tome and Principe 0
134 Dominica 0
135 Algeria 0
136 Republique du Congo 0
137 Eritrea 0
138 Libya 0

Tourism and recreation

Scores

Top 10 performers

region_name score
Aruba 100
New Zealand 100
Northern Saint-Martin 100
Antigua and Barbuda 100
Bahamas 100
Seychelles 100
Curacao 100
Montserrat 100
Saba 100
Bonaire 100

Bottom 10 performers

region_name score
191 Nigeria 2.06
192 Sudan 1.98
193 Democratic Republic of the Congo 0.43
194 Yemen 0.00
195 North Korea 0.00
196 Iran 0.00
197 Iraq 0.00
198 Syria 0.00
199 Somalia 0.00
200 Libya 0.00

Additional checks

Plot years of data for each data layer….

Table with spark plots for each region showing trends

Julie has plotting suggestion to check out

Add # of iconic species in list for each country

Comparing trend and average index scores

To obtain a more complete picture of which regions are doing well and which are doing poorly we compared the average index scores (averaged over 2012 to 2018) and the trends for each region.

Of most concern are regions that have poor scores and declining trends: Eritrea, Somalia, Ivory Coast, Nicaragua, and Lebanon.

The following is an interactive plot (region names are visible when hovering over the points) showing the relationship between average index scores and trend. The horizontal line represents the 0 trend and the vertical line is the average of the average index scores.

Pairwise Comparison of Trend

Get and tidy data for 2018 Assessment Year

final_scores <- read_csv(sprintf("../OHI_final_formatted_scores_%s.csv", dateFile))

## Select trend scores for scenario year 2018
scores_trend <- final_scores %>% 
  filter(dimension == "trend") %>% 
  filter(scenario == 2018) %>% 
  group_by(goal, region_name) %>% 
  mutate(avg_trend = mean(value)) %>% 
  ungroup() %>% 
  select(goal, dimension, region_id, region_name, score=avg_trend) %>% 
  distinct()

Create pairPlot function to plot pair-wise graph

To view plots with plotly, run this code chunk line by line after setting variables for g1 and g1. Uncomment line 1590 ggplotly(myplot)

# set col for plotting
pal <- brewer.pal(9, "PuBuGn")[3:9]

pairPlot <- function(g1, g2){ # g1="AO"; g2="BD"
  
# isolate goal 1 scores
goal1 <- scores_trend %>% 
  filter(dimension == "trend") %>% 
  filter(goal == g1) %>% 
  select(-dimension,-goal) %>% 
  na.omit() %>% 
  rename(score1=score)

# isolate goal 2 scores
goal2 <- scores_trend %>% 
  filter(dimension == "trend") %>% 
  filter(goal == g2) %>% 
  select(-dimension,-goal) %>% 
  na.omit() %>% 
  rename(score2=score)

## extract georegion r2
geo <- georegion_labels %>% 
  select(region_id=rgn_id, r2_label, r1_label)

# join goal 1 and 2, matching based on year and region id
goalscatter <- full_join(goal1, goal2, by = c("region_id", "region_name")) %>% 
  na.omit() %>% 
  left_join(geo, by="region_id") # for color coding in plot

# Plot data
myplot <- ggplot(goalscatter, aes(x=score1, y=score2)) +
  geom_point(aes(col=r1_label), alpha = 0.6) +
  scale_color_manual(values=pal) +
  theme_classic() +
  facet_wrap(r1_label~.) +
  geom_hline(yintercept = 0) +
  geom_vline(xintercept = 0) +
  stat_smooth(method=lm, se=FALSE, color="black", size = 0.5) +
  labs(title = paste("Pairwise Comparison of Trend:", g1, "and", g2, sep =" "), x = sprintf("%s",g1), y = sprintf("%s",g2)) +
  guides(fill=FALSE, color=FALSE)

# ggplotly(myplot)

ggsave(filename = paste("figures/pairwise_trend/",g1,"_",g2,"_trend.png",sep=""))

}

Create a list of goal pairs to feed into a for() loop

Plot Goals Pair-wise in a for function and save with ggsave Is there a clear trend or which quadrant do most of the points lie?

# Create a data frame with all the paired-combinations of the 10 goals
# remove ICO, Index, and subgoals
# Create vectors for the first and second part of the goal-pair combinations
goal_list <- scores_trend %>% 
         filter(goal %in% c("AO","BD","CP","CS","CW","FP","LE","NP","SP","TR"))

goal_list <- unique(goal_list$goal) 
goal_list <- combn(goal_list, 2, simplify = FALSE)
goal_df <- data.frame(goal_list)

colnames(goal_df) <- 1:ncol(goal_df)
goal_df <- data.frame(t(goal_df))
colnames(goal_df) <- c("g1","g2")

goal_df$g1 <- as.character(goal_df$g1)
goal_df$g2 <- as.character(goal_df$g2)

g1_list <- goal_df$g1
g2_list <- goal_df$g2

# create sub-folder `pairwise_trend` if it doesn't exist
if(dir.exists("figures/pairwise_trend") == "FALSE"){
  dir.create("figures/pairwise_trend")
}


## save pair plots into "figures/pairwise_trend"
mapply(pairPlot, g1 = g1_list, g2 = g2_list)
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Explore Correlations

The interesting correlations are BD-CS, BD-CP, CS-CP:

For some reason Southern Islands isn’t showing on the below images. Open up the png files to see all georegions.

View a few others.. most don’t have a strong correlation